Exploring the relationship between aristolochic acid and prostate cancer through network toxicology

Objective: To elucidate the molecular mechanisms by which aristolochic acid (AA) promotes prostate cancer (PCa) progression and to construct an AA-related prognostic model. Methods: Cellular and animal experiments, network toxicology, molecular docking, and molecular dynamics (MD) were integrated. AA targets were predicted via ChEMBL, STITCH, and SwissTargetPrediction and intersected with PCa-related genes from GeneCards, OMIM, and TTD. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment and protein-protein interaction analyses were performed. CCK-8, wound healing, Transwell, and TUNEL assays evaluated AA effects on PC3/DU145 cells, while nude mouse xenografts confirmed in vivo tumor promotion. Molecular subtyping, spatial transcriptomics, and a Cox model based on CYP1A1, CYP2E1, KLK2, and RXRA were developed and validated. Docking and MD simulations assessed AA-target interactions. Results: AA significantly enhanced proliferation and migration, inhibited apoptosis, and accelerated tumor growth in vivo . Network analysis identified 57 shared targets enriched in steroid metabolism, PI3K-Akt, and MAPK pathways, with CASP3, CYP3A4, CYP19A1, CYP2E1, and CYP1A1 as key hubs. Two molecular subtypes showed distinct metabolic and immune features. The Area Under the Curve (AUC) for the prognostic model was 0.75, indicating strong predictive accuracy, and external validation confirmed significant survival discrimination. Docking and MD revealed stable AA binding to CYP family proteins, especially CYP2E1. Conclusion: AA drives PCa progression through multi-target, multi-pathway mechanisms. The AA-based prognostic model shows translational potential for environmental carcinogen risk assessment and precision management of PCa.